Systems and Methods for Predicting and Treating Neurological Condition Relapses

ABSTRACT

Systems and methods for predicting and treating relapses for neurological conditions in accordance with embodiments of the invention are illustrated. One embodiment includes a method for predicting and treating a clinical neurological condition relapse. The method includes steps for selecting a threshold heart rate variability value for a patient suffering from a clinical neurological condition, monitoring, using a cardiac monitor, the heart rate variability of the patient over time, providing an indicator that a relapse is imminent when the heart rate variability of the patient falls below the threshold heart rate variability value, and treating the patient using a transcranial magnetic stimulation device by applying an accelerated theta burst stimulation protocol where the transcranial magnetic stimulation target is the left prefrontal dorsolateral cortex.

CROSS REFERENCE TO RELATED APPLICATIONS

The present invention claims priority to U.S. Provisional Patent Application Ser. No. 62/617,121 entitled “Systems and Methods for Personalized Clinical Applications of Accelerated Theta-Burst Stimulation” filed Jan. 12, 2018. U.S. Provisional Patent Application Ser. No. 62/617,121 is incorporated by reference in its entirety herein.

FIELD OF THE INVENTION

The present invention generally relates to predicting and treating relapses for neurological conditions.

BACKGROUND

Transcranial Magnetic Stimulation (TMS) is a non-invasive medical procedure where strong magnetic fields are utilized to stimulate specific areas of a patient's brain in order to treat a medical condition such as depression and neuropathic pain. Repeated applications of TMS in a short time frame is referred to as repetitive TMS (rTMS). Theta-burst stimulation (TBS) is a patterned form of rTMS, typically administered as a triplet of stimuli with 20 ms between each stimuli in the triplet, the triplet being repeated every 200 ms When TBS is performed continuously, this results in cortical inhibition and is termed continuous theta burst stimulation (cTBS), and when done intermittently with inter-train intervals between the triplets, this is excitatory and termed intermittent theta-burst stimulation (iTBS)

Functional Magnetic Resonance Imaging (fMRI) is a non-invasive imaging technique where neuronal activity is measured by tracking hemodynamic responses in the brain. The resting state of a brain is the measurement of neuronal activity when the patient is not performing an explicit task. The resting state of a brain can be used to explore the connectivity between various structures and regions in the brain. A common example of a resting state connectivity is the default mode network (DMN). A particular resting state connectivity between brain regions that share functional properties is called a Resting State Functional Connectivity (RSFC).

SUMMARY OF THE INVENTION

Systems and methods for predicting and treating relapses for neurological conditions in accordance with embodiments of the invention are illustrated. One embodiment includes a method for predicting and treating a clinical neurological condition relapse. The method includes steps for selecting a threshold heart rate variability value for a patient suffering from a clinical neurological condition, monitoring, using a cardiac monitor, the heart rate variability of the patient over time, providing an indicator that a relapse is imminent when the heart rate variability of the patient falls below the threshold heart rate variability value, and treating the patient using a transcranial magnetic stimulation device by applying an accelerated theta burst stimulation protocol where the transcranial magnetic stimulation target is the left prefrontal dorsolateral cortex.

In a further embodiment, the threshold heart rate variability value is selected by obtaining a baseline asymptomatic heart rate variability value for the patient and a baseline symptomatic heart rate variability value for the patient, and determining the midpoint between the asymptomatic heart rate variability value for the patient and the symptomatic heart rate variability value for the patient.

In still another embodiment, the threshold heart rate variability value is selected by calculating population average heart rate variability values.

In a still further embodiment, the indicator further includes a message to a medical scheduling system directing the medical scheduling system to schedule an appointment for treatment for the patient.

In yet another embodiment, the scheduling priority of the appointment is based upon the difference between the threshold heart rate variability value and the measured heart rate variability of the patient.

In a yet further embodiment, the clinical neurological condition is depression.

In another additional embodiment, the indicator is provided when the patient's average heart rate variability value falls below the threshold heart rate variability value for at least three days.

In a further additional embodiment, the cardiac monitor is a wrist mounted heart rate variability monitor.

In another embodiment again, the cardiac monitor is connected to a network via a wireless connection.

In a further embodiment again, indicator warns that the patient is likely to suffer a relapse.

In yet another additional embodiment, a system for predicting and treating a clinical neurological condition relapse includes steps for a cardiac monitor configured to measure heart rate variability, a processor in communication with the cardiac monitor, and a memory in communication with the processor. The memory includes a relapse prediction application, where the relapse prediction application directs the processor to select a threshold heart rate variability value for a patient suffering from a clinical neurological condition, monitor, using the cardiac monitor, the heart rate variability of the patient over time, and provide an indicator that a relapse is imminent when the heart rate variability of the patient falls below the threshold heart rate variability value. The indicator indicates that the patient will require treatment using a transcranial magnetic stimulation device to apply an accelerated theta burst stimulation protocol where the transcranial magnetic stimulation target is the left prefrontal dorsolateral cortex.

In still yet another embodiment, to select the threshold heart rate variability value, the relapse prediction application further directs the processor to obtain a baseline asymptomatic heart rate variability value for the patient and a baseline symptomatic heart rate variability value for the patient, and determine the midpoint between the asymptomatic heart rate variability value for the patient and the symptomatic heart rate variability value for the patient.

In a still yet further embodiment, the threshold heart rate variability value is selected by calculating population average heart rate variability values.

In still another additional embodiment, the indicator further includes a message to a medical scheduling system directing the medical scheduling system to schedule an appointment for treatment for the patient.

In a still further additional embodiment, the scheduling priority of the appointment is based upon the difference between the threshold heart rate variability value and the measured heart rate variability of the patient.

In still another embodiment again, the clinical neurological condition is depression.

In a still further embodiment again, the indicator is provided when the patient's average heart rate variability value falls below the threshold heart rate variability value for at least three days.

In yet another additional embodiment, the cardiac monitor is a wrist cardiac monitor.

In a yet further additional embodiment, the cardiac monitor is connected to a network via a wireless connection.

In yet another embodiment again, the method further includes steps for the transcranial magnetic stimulation device, and the treatment is applied to the patient.

Additional embodiments and features are set forth in part in the description that follows, and in part will become apparent to those skilled in the art upon examination of the specification or may be learned by the practice of the invention. A further understanding of the nature and advantages of the present invention may be realized by reference to the remaining portions of the specification and the drawings, which forms a part of this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The description and claims will be more fully understood with reference to the following figures and data graphs, which are presented as exemplary embodiments of the invention and should not be construed as a complete recitation of the scope of the invention.

FIG. 1 is a diagram illustrating an accelerated Theta-Burst Stimulation system in accordance with an embodiment of the invention.

FIG. 2 is a diagram illustrating an accelerated Theta-Burst Stimulation computing system in accordance with an embodiment of the invention.

FIG. 3 is a flow chart illustrating a method for applying personalized accelerated intermittent Theta-Burst Stimulation to a patient in accordance with an embodiment of the invention.

FIG. 4 is a stimulation schedule for accelerated intermittent Theta-Burst Stimulation in accordance with an embodiment of the invention.

FIG. 5 is a stimulation schedule for accelerated continuous Theta-Burst Stimulation in accordance with an embodiment of the invention.

FIG. 6 is a flow chart illustrating a method for generating a personalized accelerated Theta-Burst Stimulation target in accordance with an embodiment of the invention.

FIG. 7 is a set of charts illustrating a hierarchical agglomerative clustering algorithm for identifying functional subregions in a region of interest in accordance with an embodiment of the invention.

FIGS. 8A-8F are a set of charts illustrating a decision marking algorithm for deciding which functional subregion to use for an aTBS target.

DETAILED DESCRIPTION

The brain is a delicate organ, and so when medical professionals are performing procedures, tests, or otherwise interfacing with its physical structures, precise targets can be very useful. For example, when stimulating a particular portion of a brain in order to treat a patient, stimulating the wrong portion can not only result in failed or incomplete treatment, but may also create negative ramifications for the patient. As such, systems and methods for neuronavigation which can generate and/or utilize precise targets for stimulation can reduce failure rates and increase the quality of treatment.

Turning now to the drawings, systems and methods for personalized clinical applications of accelerated intermittent theta-burst magnetic stimulation in accordance with embodiments of the invention are illustrated. rTMS has been accepted as an effective treatment for clinical depression by the U.S. Food and Drug Administration for nearly a decade and single daily application of iTBS (3 min, 600 pulses) has been approved by the FDA recently. Recently, a new form of rTMS known as Theta-Burst Stimulation has been shown in its (excitatory) intermittent form (iTBS) to have an increase in efficiency over standard rTMS by reducing the amount of time the treatment needs to be applied to have a therapeutic effect. For example, 40 minutes of TMS (approximately 3000 pulses) has been shown to be non-inferior to 3 minutes of iTBS (approximately 600 pulses). However, conventional rTMS/TBS treatment regimens can take multiple weeks of sessions to achieve a stable and effective reduction in clinical symptoms. Further, conventional rTMS and iTBS techniques have targeted portions of the brain based on averages of total activity within a region. Such techniques have failed to generate targets optimized for a patient's individualized brain structure and connectivity.

Accelerated Theta-Burst Stimulation (aTBS) is a new class of rTMS variants described herein in which large quantities of TBS pulses are applied over a short period to a targeted location within the brain. This can be accomplished to produce excitation (intermittent) or to produce inhibition (continuous). Appropriate aTBS protocols in accordance with embodiments of the invention can reduce time to clinical improvement to days from weeks compared to conventional rTMS/TBS meaning patients can be hospitalized for shorter amounts of time.

Further, methods for performing aTBS can include generating personalized aTBS targets for TBS stimulation which take in to account the idiosyncrasies of a patient. Personalized targets can be generated for aTBS to maximize efficiency and/or efficacy of treatment for each patient. Because TMS coils are currently incapable of targeting structures deep within the brain, personalized aTBS targets can be generated to stimulate surface regions that are linked to deep regions, bypassing the need for deep brain stimulation.

The importance of proper targeting is highlighted by the critical symptoms of chronic mental illness. For example, in persons suffering from severe clinical depression, neurosurgery is considered a treatment of last resort. If treatment by implanted stimulators fails, the incidence of suicide increases dramatically. By using neuronavigation techniques described within, proper and effective targeting can be achieved prior to surgery to increase the success rate of the procedure in numerous ways. In many embodiments, aTBS is performed on a trial basis to test the efficacy of a neuronal implant prior to surgery by stimulating a selected target or set of targets. In a variety of embodiments, targets with the best test results are selected as targets for an implantable system. In the case that no target with a high enough chance of success for the patient and/or medical professionals is found, then the invasive surgery can be avoided entirely. As magnetic stimulation and electric stimulation are closely related, implanted neurostimulators can be programmed to provide electrical stimulation protocols similar to aTBS magnetic protocols with similar results. Systems for performing aTBS and neuronavigation targeting methods are discussed below.

aTBS Systems

aTBS systems in accordance with embodiments of the invention can acquire neuroimaging data of a patient's brain and generate personalized aTBS targets for treatment with aTBS devices. In many embodiments, aTBS systems include aTBS devices. A conceptual diagram of an aTBS system in accordance with an embodiment of the invention is shown in FIG. 1. aTBS system 100 includes an aTBS Device 110. aTBS devices can be any TMS coil that is capable of delivering magnetic pulses with the frequencies, intensities, and durations required by aTBS. aTBS devices can be, but are not limited to, a Magventure X100 produced by MagVenture of Farum, Denmark, Magstim coils produced by The Magstim Company Limited of Whitland, United Kingdom, Neurosoft coils produced by Neurosoft of Utrecht, Netherlands, and the Brainsway H7-deep-TMS system, or the H1 Coil TMS device, both of which are produced by Brainsway Ltd. of Jerusalem, Israel. However, any TMS coil can be used as appropriate to the requirements of specific applications of embodiments of the invention. Further, aTBS systems can include more than one aTBS device in order to better target different regions of the brain.

In numerous embodiments, neuronavigation systems can be used to correctly place the aTBS device relative to the aTBS target. Neuronavigation systems capable of displaying the aTBS target can include Localite TMS Navigator produced by Localite GmbH of Sankt Augustin, Germany, visor2 systems produced by ANT Neuro of the Netherlands, and BrainSite TMS Navigation produced by Rogue Solutions Ltd. of Cardiff, Wales. However, any kind of neuronavigation device capable of assisting with the placement of the aTBS device can be utilized as appropriate to the requirements of specific applications of embodiments of the invention. In numerous embodiments, the neuronavigation system is provided and/or generates a target or set of targets generated using targeting processes described below.

aTBS system 100 further includes an aTBS computing system 120 and a brain imaging device 130. aTBS computing systems can be implemented as one or more computing devices capable of processing brain image data and generating personalized aTBS targets. Brain imaging devices are capable of obtaining imaging data describing a patient's brain. Brain imaging devices can obtain structural, functional imaging data, and/or resting state imaging data. In numerous embodiments, brain imaging devices are magnetic resonance imaging (MRI) machines, functional MRI machines (fMRI), or any other brain scanning device as appropriate to the requirements of specific applications of embodiments of the invention. Functional imaging data can be obtained by scanning a patient using an fMRI scanner or other brain imaging device, while the patient is performing specific tasks and/or is provided specific stimuli. Resting state imaging data can be obtained by scanning a patient using an fMRI scanner, or other brain imaging device while the patient is not performing any task.

Additionally, aTBS system 100 includes interface device 140. Interface devices can be, but are not limited to, computers, smart-phones, tablet computers, smart-watches, or any other type of computing interface device as appropriate to the requirements of specific applications of embodiments of the invention. In many embodiments, interface devices are used to interface with brain imaging devices, aTBS computing systems, and/or aTBS devices. In numerous embodiments, aTBS computing systems and interface devices are implemented using the same physical device. aTBS system 100 includes a network 150 connecting aTBS device 110, aTBS computing system 120, and interface device 130. In a variety of embodiments, the network is the Internet. However, any network such as, but not limited to, an intranet, a local area network, a wire area network, or any other computing network capable of connecting computing devices can be used as appropriate to the requirements of a given application.

While a specific aTBS system is illustrated with respect to FIG. 1, one of ordinary skill in the art would appreciate that numerous different configurations of aTBS systems are possible, including, but not limited to, system architectures where different devices are not connected, or not all connected, by a network. aTBS computing systems are discussed below.

aTBS Computing Systems

aTBS computing systems in accordance with embodiments of the invention can generate personalized aTBS targets for a given patient. A conceptual illustration of an aTBS computing system in accordance with an embodiment of the invention is shown in FIG. 2. aTBS computing system 200 includes a processor 210 in communication with a communications interface 220 and a memory 230. In numerous embodiments, aTBS computing systems include multiple processors, multiple memories, and/or multiple communications interfaces. In a variety of embodiments, components of aTBS computing systems are distributed across multiple hardware platforms.

Processor 210 can be any type of computational processing unit, including, but not limited to, microprocessors, central processing units, graphical processing units, parallel processing engines, or any other type of processor as appropriate to the requirements of specific applications of embodiments of the invention. Communications interface 220 can be utilized to transmit and receive data from other aTBS computing systems, brain imaging devices, aTBS devices, and/or interface devices. Communications interfaces can include multiple ports and/or communications technologies in order to communication with various devices as appropriate to the requirements of specific applications of embodiments of the invention.

Memory 230 can be implemented using any combination of volatile and/or non-volatile memory, including, but not limited to, random access memory, read-only memory, hard disk drives, solid-state drives, flash memory, or any other memory format as appropriate to the requirements of specific applications of embodiments of the invention. In numerous embodiments, the memory 230 stores a variety of data, including, but not limited to, an aTBS targeting application 232 and imaging data 234. In many embodiments, the aTBS targeting application and/or the imaging data are received via the communications interface. Processor 210 can be directed by the aTBS targeting application to perform a variety of aTBS processes, including, but not limited to, processing imaging data and generating aTBS targets.

Although specific architectures for aTBS computing systems in accordance with embodiments of the invention are conceptually illustrated in FIG. 2, any of a variety of architectures, including, but limited to, those that direct aTBS devices to perform aTBS, direct brain imaging devices to capture imaging data, and/or utilize different hardware capable of performing similarly to the above can also be utilized. Furthermore, aTBS computing systems can be implemented on multiple servers within at least one computing system. For example, aTBS computing systems can be implemented on various remote “cloud” computing systems as appropriate to the requirements of specific applications of embodiments of the invention. However, one of ordinary skill in the art would appreciate that a “computing system” can be implemented on any appropriate computing device, including, but not limited to, a personal computer, a server, a cluster of computing devices, and/or a computing device incorporated into a medical device. In numerous embodiments, aTBS computing systems are implemented as part of an integrated aTBS device. A discussion of various aTBS processes is found below.

Processes for Performing aTBS

Traditional rTMS procedures utilize large numbers of pulses over a long period of time. Traditional TBS utilizes patterned pulses which reduce the number of pulses required to achieve similar results to rTMS. However, neither method can produce clinically useful changes in under a week. Processes for performing aTBS in accordance with embodiments of the invention can include accelerated treatment regimens compared to conventional rTMS/TBS methods.

Turning now to FIG. 3, a process for performing aTBS in accordance with an embodiment of the invention is illustrated. Process 300 includes obtaining (310) patient brain imaging data. In numerous embodiments, patient brain imaging data includes anatomical imaging data, resting state imaging data, functional imaging data, and/or any other brain imaging data as appropriate to the requirements of specific applications of embodiments of the invention. Anatomical imaging data can include, but is not limited to, structural MRI scan data, diffusion tensor imaging data, computed tomography scans, and/or any other structural image of a brain produced by an imaging technology. Functional imaging data is imaging data that describes the neuronal activation of a brain. Resting state imaging data is imaging data that describes the neuronal activation of a brain during a resting state. In numerous embodiments, functional imaging data and resting state imaging data are obtained from fMRI scans.

Patient brain imaging data can be used to generate (320) personalized aTBS targets. aTBS can be applied (330) to the patient at the personalized aTBS target. In many embodiments, the TMS coil utilized to apply the aTBS treatment is focused on the target by manipulating the placement of coil and/or the coil angle, and thus the orientation of the magnetic field. In many embodiments, aTBS is applied according to a pre-determined protocol. aTBS protocols can vary depending on numerous factors, including, but not limited to, the severity of condition, whether or not aiTBS or acTBS is used, or any other factors as appropriate to the requirements of specific applications of embodiments of the invention. aTBS protocols can include a set of parameters describing the form of iTBS to be applied, and a schedule describing when the iTBS is applied. In numerous embodiments, include e-field measurements are used to inform the choice of coil angle.

In numerous embodiments, aiTBS schedules involves applying iTBS pulses for multiple sessions per day, for several days. In a variety of embodiments, the iTBS pulse parameters involve 3-pulse, 50 Hz pulses at 5 Hz for 2 second trains, with trains every 10 seconds for 10 minute sessions (1,800 total pulses per session). In many embodiments, aiTBS schedules describe conducting 10 sessions per day with 50-minute intersession intervals for 5 consecutive days (18,000 pulses per day, 90,000 total pulses).

However, a wide range of parameters can be used, for example, iTBS pulse parameters can involve any number of pulses of between 20 Hz and 70 Hz, at 3 Hz to 7 Hz, with trains every 4 seconds to 10 seconds, with intersession intervals between 25 minutes and 120 minutes. An example schedule for treatment using aiTBS in accordance with an embodiment of the invention is illustrated in FIG. 4.

In a variety of embodiments, the cTBS pulse parameters involve 3-pulse trains with 50 Hz pulses at 5 Hz for 40 second sessions (600 total pulses per session). In a variety of embodiments, the cTBS pulse parameters involve 3-pulse, 30 Hz pulses at 6 Hz for 44 second sessions (800 total pulses per session). In many acTBS embodiments, 30 sessions are applied per day with 15-minute intersession intervals for 5 consecutive days (18,000 pulses per day, 90,000 total pulses). However, a wide range of parameters can be used, for example, cTBS parameters can involve any number of pulses of 20 Hz to 70 Hz, at 3 Hz to 7 Hz with an intersession interval of between 10 and 50 minutes. An example schedule for treatment using acTBS in accordance with an embodiment of the invention is illustrated in FIG. 5.

However, TBS parameters and schedules for an aTBS protocol can be varied. For example, the number of pulses or frequency of sessions can be increased or decreased depending on the refractoriness of the patient and/or the severity of the clinical condition (i.e. for less severe cases of depression, fewer pulses can be effectively utilized, thereby further reducing treatment times). In numerous embodiments, the number of pulses per session range from approximately 600 to 2,400 depending on the type of aTBS applied. In a variety of embodiments, the number of sessions for aiTBS range from 3 to 15 sessions per day. In many embodiments, the number of sessions for acTBS range from 10-40 sessions per day. Nonetheless, one of ordinary skill in the art would appreciate that any number of pulses and lengths of intersession intervals can be used as appropriate to the requirements of specific applications of embodiments of the invention to fit the needs of an individual patient.

Changes in the patient's resting state can be measured (340) using methods similar to those described above with respect to obtaining resting state imaging data. If there is sufficient change (350) in resting state to result in the clinical results desired, then treatment can optionally end. If there has not been a sufficient change (350) in the patient's resting state, additional aTBS treatment sessions can be performed.

Specific processes for performing aTBS in accordance with embodiments of the invention are described above and shown with respect to FIG. 3; however, any number of processes, including, but not limited to, those that use alternate number of pulses, sessions, frequencies, imaging methods, and/or degree of change in resting state, can be utilized as appropriate to the requirements of a specific application in accordance with embodiments of the invention.

Generating aTBS Targets

Every person has a unique brain structure and connectivity. While the average across all brains has been used to generate targets, this ignores each patient's idiosyncrasies. Processes for performing aTBS in accordance with embodiments of the invention can include generating personalized aTBS targets to increase the efficiency and efficacy over standard iTBS treatment. Turning now to FIG. 6, a process for generating an aTBS target in accordance with an embodiment of the invention is illustrated.

Process 600 includes obtaining (610) brain imaging data. In many embodiments, brain imaging data includes structural imaging data, resting state imaging data, and/or functional imaging data similar to those described above. In numerous embodiments, brain imaging data is preprocessed. Preprocessing steps can include, but are not limited to, physiological noise regression, slice-time correction, motion correction, co-registration, band-pass filtering, de-trending, and/or any other preprocessing step as appropriate to the requirements of a given application. Brain imaging data can be used to map (620) standardized regions of interest (ROIs) onto the individualized anatomy. In numerous embodiments, mapping standardized regions of interest onto individualized anatomy is performed by aligning structural imaging data to a standardized brain atlas, and reversing the alignment parameters to map a standardized ROI onto an individual's anatomy. Standardized brain atlases can define one or more brain regions that are targetable with TMS. In a variety of embodiments, the brain atlas defines brain regions that are targetable with a specific TMS coil. In numerous embodiments, task-based fMRI is used to restrict and/or broaden the extent of the brain regions that are to be targeted with TMS.

In many embodiments, a personalized map of functional activations within ROIs is generated (630). In a variety of embodiments, functional activations within ROIs are identified using functional imaging data and/or resting state imaging data. The map of individualized ROIs and resting state imaging data can be used to generate (640) a personalized map of functional subregions within ROIs. In numerous embodiments, functional subregion is defined as a brain region within ROIs in which all temporal brain activity is highly correlated across the spatial extent of the subregion. In many embodiments, the resting state imaging data can be used to generate resting state functional connectivity data describing the functional connectivities between regions of the brain. In numerous embodiments, resting state data extracted from the resting state imaging data such as, but not limited to, functional connectivities and task based neuronal activations can be mapped to the personalized regions of interest. In numerous embodiments, a personalized map of task based functional activations within the ROIs can be utilized to refine or expand a functional connectivity based personalized map of functional subregions. Task-based functional imaging data can lead to more refined and/or higher quality analysis of functional connectivities between subregions.

Additionally, resting state functional connectivity data across ROIs can be further parcellated into functional subregions such that each parcellated subregion is made up of homogeneous brain activity as measured over the course of a resting state scan. In numerous embodiments, parcellation is achieved using hierarchical clustering, such as, but not limited to, hierarchical agglomerative clustering. However, any parcellation method can be used as appropriate to the requirements of a given embodiment. In a variety of embodiments, the size of a subregion is determined by the number of voxels that act in a homogenous fashion. As such, the size of a subregion can vary across different ROIs depending on the function of the subregion, the structure of the brain in which the subregion is located, the idiosyncrasies of the patient's brain, and any of a number of other factors that impact the connectivity and reactivity of the brain.

The relationships between the functional subregions can be determined (650) using a variety of techniques. In numerous embodiments, the voxel time course that is most highly correlated with the median of all voxel time courses within a functional subregion can be selected as reflecting the activity of the functional subregion. In many embodiments, taking a simple average of all time courses for voxels within the subregion can be used, although this tends to be less robust. By reducing each functional subregion to a single time course, an accurate representation of the typical time course of brain activity that occurs within groups of homogenous voxels can be determined.

An example grouping and correlation calculation in accordance with an embodiment of the invention is illustrated in FIG. 7. Further, single, representative time courses enable the calculation of correlation coefficients between functional subregions that exist within the same ROI. However, these correlation coefficients will tend to be low because groups of voxels with high correlation coefficients tend to be organized into the same subregion. The process of reducing each functional subregion identified through parcellation to a single time course further allows for the calculation of correlation coefficients between all functional subregions discovered across multiple ROIs across the brain. For example, multiple subregions within the left dorsolateral prefrontal cortex can be correlated with multiple subregions within the cingulate cortex, although any number of different subregions within any number of different brain structures can be correlated. An example set of correlations across multiple ROIs in accordance with an embodiment of the invention is illustrated in FIGS. 8A-8F.

Process 600 further includes parameterizing (660) functional subregions. Subregions can be assigned values for various parameters such as, but not limited to, size of the subregion i.e. the number of voxels or volume of brain, the spatial concentration of voxels i.e. the number of voxels divided by the average Euclidean 3 dimensional spatial distance between all voxels that make up the subregion, the functional relationships between a given subregion and other functional subregions (i.e. voxel-weighted average correlation coefficients), and/or accessibility of the subregion from the surface of the brain such as, but not limited to, the depth of the subregion and/or whether or not the subregion is obscured by another subregion. However, any number of parameters, or parameter weighting schemes can be used as appropriate to the requirements of a given application.

Functional subregion parameters can be used to calculate (670) target quality scores for each the functional subregions. In numerous embodiments, the target quality score for each subregion is a function of weighted combinations of subregion parameters. In many embodiments, the target quality scores are generated by determining the surface influence (voxel size-weighted correlation coefficients) for a set of subregions. In many embodiments, the surface influence for a given subregion is the sum of a two-dimensional matrix of Spearman's or Pearson's correlation coefficients derived from a hierarchical clustering algorithm describing the correlation coefficients between a surface ROI subregion and all of the deep ROI subregions. In numerous embodiments, the first ROI is located near the surface of the brain, and the second ROI is located deeper within the brain tissue. However, difference in depth is not a requirement for ROI comparisons. Surface ROI subregions that have positive influence on deep subregions and surface ROI subregions that have negative influence on deep subregions can be determined based on the surface influence calculations. In some embodiments, depending on the type of treatment, only positive or only negative influencing subregions may be further considered as targets. In numerous embodiments, the surface subregion concentration can be calculated. In many embodiments, surface subregion concentration can be calculated by dividing the number of voxels in a surface subregion by the surface subregion non-cohesion as measured by the average Euclidian distance between voxels in the subregion. The surface subregion concentration can be further normalized to assist in interpretation. In numerous embodiments, target quality scores are determined by multiplying the surface subregion concentration by the surface influence of a particular subregion. As such, target quality scores can reflect both the potential impact of stimulating a given surface subregion on a deep subregion as well as the ability and efficiency with which the subregion can be targeted by the TMS coil. However, any number of methods can be used to generate target quality scores as appropriate to the requirements of specific applications of embodiments of the invention.

Personalized aTBS target can be generated (680) based on the target quality scores. In numerous embodiments, the highest quality targets are selected as the personalized aTBS target. In a variety of embodiments, more than one target can be selected.

Specific processes for generating aTBS targets in accordance with embodiments of the invention are described above and shown with respect to FIG. 6; however, any number of processes, including, but not limited to, those that use different and/or fewer types of imaging data, use different subregion parameters, different parcellation methods, and/or any other quality generation method can be utilized as appropriate to the requirements of a specific application in accordance with embodiments of the invention.

Clinical Treatment Using aTBS

Clinical evaluation as determined that aTBS can be used to treat a variety of different medical conditions, both mental and physical. For example, aiTBS is effective at reducing suicide ideation and lessening symptoms of depression when performed over the left dorsolateral prefrontal cortex (L-DLPFC). Patients undergoing aiTBS over L-DLPFC treatment are often able to be released from hospitalization in between 2 and 5 days. Further, aiTBS over L-DLPFC can be used to increase heart rate variability (HRV), which is correlated with numerous psychiatric conditions as well as various types of cancer, various heart diseases, and inflammatory conditions. Heart rate variability (HRV) is the physiological phenomenon of variation in the R-R interval on the electrocardiogram (ECG).

In a variety of embodiments, heart rate variability is treated by targeting the L-DLPFC in such a way as to effect changes in the Subcallosal Cingulate Cortex (SCC) which in turn can effect changes in the vagus nerve which can be used to effect heart rate. Heart rate variability can be used as a neurophysiological biomarker that is treatment-responsive for resolution of prefrontal dysregulation of sympathetic/parasympathetic balance. For example, in many embodiments, heart rate decelerations are used to confirm target engagement. Similarly, ongoing recording of heart rate decelerations can be used for ongoing target confirmation. Identified functional subregions can be used to find the best available targets for influencing heart rate variability. This can be done by iteratively testing each functional subregion with in an ROI TMS stimulation for its effects on heart rate or heart rate variability.

Pre-Symptomatic Treatment Triggers

In many situations, a key concern for patients with mental conditions is the lack of knowledge as to when previous treatments have worn off and when new treatments need to be initiated. Systems and methods described herein can detect when additional treatment will be required prior to patients becoming symptomatic. For example, low heart rate variability is an indicator for a number of different clinical mental and physical conditions, such as, but not limited to, PTSD, depression, suicidal ideation, heart disease, and/or sepsis. As discussed above, aTBS protocols can be utilized to increase HRV. aTBS over left dorsolateral prefrontal cortex (L-DLPFC) projects to the anterior cingulate cortex (ACC) (the HRV pacemaker) and a single train of aTBS will acutely decelerate the heart rate.

To take advantage of these properties, in numerous embodiments, systems and methods for aTBS include an HRV monitor and are capable of performing relapse prediction processes. Relapse prediction processes utilize HRV information to determine whether or not a patient is likely to suffer an imminent relapse. HRV monitors can be connected to a larger system such as, but not limited to, those described above via a network connection. For example, many HRV monitors can connect to other devices using wired and/or wireless connections that enable data to be transferred. HRV monitors are widely commercially available in consumer goods, for example wrist mounted HRV monitors such as the iWatch by Apple located in Cupertino, Calif. are purchasable outside of a medical context but provide medical grade functionality. However any number of cardiac monitors can be used. In many embodiments, when a patient's HRV reaches and/or passes a threshold value, an indicator can be provided to the patient that it is time to receive another treatment session. In some embodiments, when the indicator can include interfacing with a medical scheduling system to alert the responsible medical team that the patient is in need of additional treatment. In many cases, the medical scheduling system can automatically generate an appointment for treatment. In a variety of embodiments, the severity of the deviation from a healthy HRV can be transmitted in the indicator and impact the priority of the appointment scheduling.

In a variety of embodiments, the threshold value is a value selected based upon population average HRVs. However, threshold values can be personalized to individual patients by recording baseline HRV values for that individual during healthy and/or symptomatic states. For example, in the case of depression, a measurement of HRV can be taken when the patent is symptomatic, and a measurement of HRV can be taken when the person is euthymic. In many embodiments, the threshold is then defined HRV is defined as the midpoint between the patient's highest value (euthymia) and their lowest value (depression). In numerous embodiments, if the patient experiences more than three days of HRV at or below the threshold value, then an indicator can be presented suggesting that the user return to their medical care practitioner for treatment. While a specific method for personalizing threshold values is described above, any number of methods for determining threshold values at which there is a high degree of certainty that the patient will become symptomatic in the near future can be utilized as appropriate to the requirements of a given application of an embodiment of the invention. Similarly, any number of time values can be used as a trigger for providing indicators when the patient's HRV drops below the threshold value. Additionally, in a variety of embodiments, HRV measurements are average measurements taken over a predetermined time window.

In many embodiments, calculations described above are performed by the cardiac monitor. However, data from the cardiac monitor can be sent to secondary computing devices for processing. In numerous embodiments, a relapse prediction application is used to configure a processor, whether of the cardiac monitor and/or a secondary computing device, to perform the relapse prediction processes described above. By utilizing HRV monitors, patient's worries can be assuaged using non-invasive but medically sound predictive techniques.

Implantable aTBS

In numerous embodiments, for severe clinical conditions, aTBS can be applied by implanted neurostimulators. aTBS protocols can be adapted to electrical stimulation instead of magnetic stimulation, and stimuli can be applied long-term without the need for external magnetic stimulation. Further, external aTBS with an rTMS device can be performed to probe for the correct target choice and therapeutic efficacy, and then neurostimulators can be implanted over the target determined to have the most likely positive results either epidurally or subdurally. In numerous embodiments, neurostimulators provide stimulation and record brain activity. By providing both stimulation and recording channels, a closed loop system can be achieved. Based on recorded brain activity, stimulation can be modulated to either increase or decrease the amount of aTBS provided or change stimulation target. Further, machine learning algorithms can be used to adapt to the optimal stimulation strategy for that person. For example, if positive neurological activity is recorded, stimulation can cease until pathological neurological activity is detected. In numerous embodiments, recording brain activity is achieved using standard electrocortiocographical (ECoG) methods.

In a variety of embodiments, multiple stimulation electrodes can be implanted over multiple targets, either for aTBS applications or otherwise. Recording activity can be used to selectively activate or deactivate different electrodes in response to different responses for safety reasons as well as clinical as for aTBS applications. For example, if a seizure activity is detected in a particular area of the brain, stimulation electrodes in that region can be deactivated and/or utilized to induce normal brain activity.

Neurostimulators can also be used with open loop parameters, where a static protocol is utilized to maintain a desired brain activation pattern. In many embodiments, the static protocol can be adapted or modified via an external controller. Many neurostimulator devices use a combination of open loop and/or closed looped formats as appropriate to the requirements of a given application of an embodiment of the invention.

As noted above, neurostimulators can be placed on targets selected using neuronavigation techniques above. However, when the surgeon is placing the neurostimulator, it can be useful to verify placement during the procedure. In numerous embodiments, organ responses to brain stimulation can be used to verify placement. For example, an electrode placed on the L-DLPFC can affect heart rate, and by measuring heart rate, stimulation of the L-DLPFC can be verified. As L-DLPFC stimulation can also effect depression, this can be a useful clinical tool for implantation of neurostimulators to treat clinical depression.

Electrocorticography (epidural/subdural), EEG, and NIRS can be correlated with heart rate and heart rate variability, and both can be used as closed loop indicators of ongoing efficacious stimulation.

Further, as aTBS is able to be performed to have an excitatory (aiTBS) or an inhibitory (acTBS) effect, with correct target selection, various neurological changes can be implemented that can have impacts beyond the brain. For example, manipulating the hypothalamus can impact cortisol secretion by altering the hypothalamic-pituitary-adrenal axis. Additionally, post-injury, neurological plasticity can be increased by exciting and inhibiting different neuronal connections. Similarly, aTBS can be used to promote learning and skill acquisition by increasing neurological plasticity directly in targeted neuronal networks or elsewhere.

Although specific systems and methods for performing aTBS are discussed above, many different systems and methods can be implemented in accordance with many different embodiments of the invention. It is therefore to be understood that the present invention may be practiced in ways other than specifically described, without departing from the scope and spirit of the present invention. Thus, embodiments of the present invention should be considered in all respects as illustrative and not restrictive. Accordingly, the scope of the invention should be determined not by the embodiments illustrated, but by the appended claims and their equivalents. U.S. patent application Ser. No. 16/215,512 titled “Systems and Methods for Personalized Clinical Applications of Accelerated Theta-Burst Stimulation” filed Dec. 10, 2018, and U.S. patent application Ser. No. 16/215,519 titled “Systems and Methods for Personalized Treatment of Neurological Conditions using Implantable Neurostimulators” filed Dec. 10, 2018, and U.S. patent application Ser. No. 16/215,475 titled “Systems and Methods for Clinical Neuronavigation” filed Dec. 10, 2018 are incorporated by reference in their entireties herein for all purposes. 

What is claimed is:
 1. A method for predicting and treating a clinical neurological condition relapse, the method comprising: selecting a threshold heart rate variability value for a patient suffering from a clinical neurological condition; monitoring, using a cardiac monitor, the heart rate variability of the patient over time; providing an indicator that a relapse is imminent when the heart rate variability of the patient falls below the threshold heart rate variability value; and treating the patient using a transcranial magnetic stimulation device by applying an accelerated theta burst stimulation protocol where the transcranial magnetic stimulation target is the left prefrontal dorsolateral cortex.
 2. The method of claim 1, wherein the threshold heart rate variability value is selected by: obtaining a baseline asymptomatic heart rate variability value for the patient and a baseline symptomatic heart rate variability value for the patient; and determining the midpoint between the asymptomatic heart rate variability value for the patient and the symptomatic heart rate variability value for the patient.
 3. The method of claim 1, wherein the threshold heart rate variability value is selected by calculating population average heart rate variability values.
 4. The method of claim 1, wherein the indicator further comprises a message to a medical scheduling system directing the medical scheduling system to schedule an appointment for treatment for the patient.
 5. The method of claim 4, wherein the scheduling priority of the appointment is based upon the difference between the threshold heart rate variability value and the measured heart rate variability of the patient.
 6. The method of claim 1, wherein the clinical neurological condition is depression.
 7. The method of claim 1, wherein the indicator is provided when the patient's average heart rate variability value falls below the threshold heart rate variability value for at least three days.
 8. The method of claim 1, wherein the cardiac monitor is a wrist mounted heart rate variability monitor.
 9. The method of claim 1, wherein the cardiac monitor is connected to a network via a wireless connection.
 10. The method of claim 1, wherein indicator warns that the patient is likely to suffer a relapse.
 11. A system for predicting and treating a clinical neurological condition relapse, the method comprising: a cardiac monitor configured to measure heart rate variability; a processor in communication with the cardiac monitor; and a memory in communication with the processor, comprising a relapse prediction application, where the relapse prediction application directs the processor to: select a threshold heart rate variability value for a patient suffering from a clinical neurological condition; monitor, using the cardiac monitor, the heart rate variability of the patient over time; and provide an indicator that a relapse is imminent when the heart rate variability of the patient falls below the threshold heart rate variability value; where the indicator indicates that the patient will require treatment using a transcranial magnetic stimulation device to apply an accelerated theta burst stimulation protocol where the transcranial magnetic stimulation target is the left prefrontal dorsolateral cortex.
 12. The system of claim 11, wherein to select the threshold heart rate variability value, the relapse prediction application further directs the processor to: obtain a baseline asymptomatic heart rate variability value for the patient and a baseline symptomatic heart rate variability value for the patient; and determine the midpoint between the asymptomatic heart rate variability value for the patient and the symptomatic heart rate variability value for the patient.
 13. The system of claim 11, wherein the threshold heart rate variability value is selected by calculating population average heart rate variability values.
 14. The system of claim 11, wherein the indicator further comprises a message to a medical scheduling system directing the medical scheduling system to schedule an appointment for treatment for the patient.
 15. The system of claim 14, wherein the scheduling priority of the appointment is based upon the difference between the threshold heart rate variability value and the measured heart rate variability of the patient.
 16. The system of claim 11, wherein the clinical neurological condition is depression.
 17. The system of claim 11, wherein the indicator is provided when the patient's average heart rate variability value falls below the threshold heart rate variability value for at least three days.
 18. The system of claim 11, wherein the cardiac monitor is a wrist cardiac monitor.
 19. The system of claim 11, wherein the cardiac monitor is connected to a network via a wireless connection.
 20. The system of claim 11, further comprising the transcranial magnetic stimulation device, and the treatment is applied to the patient. 